Image quality can affect any of a number of imaging applications. For example, surveillance platforms can be affected by the ultimate resolution afforded by the imaging system, including optics and image processing subsystems. One approach to increasing resolution involves utilizing improved optics, but this can be cost-prohibitive. Another approach toward improving the resolution of surveillance images involves super-resolving images. Super-resolution (SR) is a technique that enhances the resolution of an imaging system. There are both single-frame and multiple-frame variants of SR.
The aim of SR is to estimate a high resolution image from several low resolution images of the same scene. SR gains result from a combination of noise reduction, de-aliasing and deblurring, or high-spatial frequency restoration. SR has a long history, primarily of applications to whole images of static scenes.
Multiple image frame SR algorithms are designed to combine several images of a scene to produce one higher resolution image. Before a set of images or frames can be super-resolved, they are typically registered. In general, image super-resolution algorithms model the image registration as translations or homographies. In aerial surveillance applications, such a registration model generally works well for a static ground scene. However, when objects are moving in the scene, they are not properly registered by this model and the effect on the super-resolved image is distortion in the local area of the moving object.